Network pharmacology analysis reveals potential targets and mechanisms of proton pump inhibitors in breast cancer with diabetes

Breast cancer and diabetes are significant health challenges, and effective treatments for both diseases are lacking. Proton pump inhibitors (PPIs) have demonstrated anticancer and hypoglycemic effects, but their mechanisms of action are not yet fully understood. We used the GeneCards and PharmMapper databases to identify therapeutic targets for diabetes, breast cancer and PPIs. We identified common targets and constructed a regulatory network of diseases and drugs using the STRING database and Cytoscape software. We also explored the binding between small molecule ligands and protein receptors using Discovery Studio software. We identified 33 shared targets for breast cancer, diabetes, and PPIs including lansoprazole, omeprazole, and pantoprazole, which play a critical role in fatty acid transport, insulin resistance, apoptosis, and cancer-related signaling pathways. Our findings demonstrated that PPIs had a strong affinity for AKT1 and MMP9. This study provides insights into the mechanisms of action of PPIs in breast cancer and diabetes and identifies AKT1 and MMP9 as critical targets for future drug development. Our findings highlight the potential of PPIs as a novel therapeutic approach for these challenging diseases.


Common target of proton pump inhibitors in the treatment of dual diseases. Subsequently,
we looked for the predicted targets of lansoprazole, omeprazole, and pantoprazole and the common targets of genes related to breast cancer and diabetes respectively, and then took the common target genes of three kinds of drugs as our research objects. We used the intersect function in R software (version 4.0.3) which was a free and open-source programming language and software environment for statistical computing and graphics to screen the common targets of breast cancer, diabetes and proton pump inhibitor representative drugs and invoked the R package Venn to visualize the results of intersection genes and output the intersection genes.
GO and KEGG enrichment analysis. Enrichment analysis was conducted to identify significantly enriched biological processes and signaling pathways among the intersection of drug and disease targets. Firstly, the gene symbols were converted to Ensembl IDs using the org.Hs.eg.db package. Next, Gene Ontology (GO) analysis was performed using the "enrichGO" function in the R package clusterProfiler, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the "enrichKEGG" function 26 . To filter for enriched categories or pathways, a significance threshold of p value < 0.05 and q-value < 0.05 was applied, and the degree of enrichment was represented using the − log10 transformed p value. Categories with a p value and q-value less than 0.05 were considered significant. The top 10 enriched results were visualized for both KEGG analysis and the three modules of GO enrichment analysis, which included biological processes (BP), cellular components (CC), and molecular functions (MF). The enriched categories and pathways identified in this study provide insights into the biological mechanisms underlying the drug and disease targets and may help in the identification of potential therapeutic targets. www.nature.com/scientificreports/ Construction of a regulatory network and screening of core hub genes. To construct a comprehensive regulatory network for crossover genes, we utilized the STRING online database with a medium confidence threshold set at > 0.4 to obtain protein-protein interaction information 27 . Subsequently, we imported the interaction data into Cytoscape software version 3.9.1 to visualize the regulatory network of diseases, drugs, and signaling pathways 28 . To identify key components in the network, we used the CytoNCA tool in Cytoscape, which analyzed the network data based on various criteria such as Betweenness, Closeness, Degree, Eigenvector, LAC, and Network 29 . Subsequently, transcriptome information of 459 normal samples from GTEX and 113 normal samples from TCGA and 1101 patients with breast cancer was collected to study the differential expression of the above six core genes. AKT1 and MMP9 were finally selected as potential therapeutic targets. This filtering approach allowed us to identify the most important and influential nodes in the network, which could play a critical role in the pathogenesis of the studied diseases or serve as potential therapeutic targets.  15.743137 (AKT1) and 11.258874 (MMP9). The ligands were docked into the active site of the protein using the CDOCKER algorithm with default parameters. The number of runs was set to 10, and the best pose of each ligand was selected based on the CDOCKER interaction

Results
Acquisition of co-targets for proton pump inhibitors and diseases. In this study, a total of 1838 targets for breast cancer and 417 targets for diabetes were obtained from the GeneCards database using a relevance score of ≥ 10. The 2D structures of omeprazole, lansoprazole, and pantoprazole were downloaded from PubChem and corresponding predicted targets were obtained from the PharmMapper database, which uses three major pharmacophore groups to make target predictions. The predicted targets of the three proton pump inhibitors were intersected with the disease targets, resulting in the identification of 35 common targets of omeprazole and breast cancer and diabetes ( Fig. 2A), 34 common targets of pantoprazole and breast cancer and diabetes (Fig. 2B), and 36 common targets of lansoprazole and breast cancer and diabetes (Fig. 2C). Finally, 33 intersection genes were identified as common targets of proton pump inhibitors and the two diseases (Fig. 2D).
Enrichment analysis identifies key biological processes and pathways related to proton pump inhibitor targets. After obtaining the common targets of proton pump inhibitors and diseases, we performed GO and KEGG enrichment analyses to explore the biological processes involved in the identified genes. We investigated the overrepresented biological processes and pathways using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The GO enrichment analysis revealed several significantly enriched biological processes (BP), molecular functions (MF), and cellular components (CC) among the differentially expressed genes. The top ten enriched GO terms in each category are shown in Table 1.
From the perspective of biological processes, the therapeutic targets were mainly enriched in fatty acid transport, cell proliferation, and regulation of apoptotic signaling, metabolic processes of small molecules, responses www.nature.com/scientificreports/ to reactive oxygen species, etc. However, for cellular components, there was no significant enrichment of target genes (p > 0.05). For molecular functions, the targets were mostly enriched in the activity of nuclear receptors, ligand − activated transcription, nuclear hormone receptors (Fig. 3). The KEGG pathway analysis revealed several significantly enriched pathways among the differentially expressed genes, including prostate cancer, diabetic cardiomyopathy, lipid and atherosclerosis, insulin resistance. The top ten enriched pathways are shown in Table 2. These pathways are known to play important roles in Signaling pathways associated with the pathogenesis of diabetes and cancer development and progression (Fig. 4). Overall, the GO and KEGG enrichment analyses provided insights into the biological processes and pathways that are dysregulated in the studied condition and could provide potential therapeutic targets for further investigation.

Identification of specific targets of proton pump inhibitors via PPI network analysis. After
constructing the regulatory network using the STRING database and Cytoscape software, we obtained scores for each node using the CytoNCA tool, which calculates various network centrality measures including betweenness, closeness, degree, eigenvector, LAC, and network. The scores of the 33 targets are shown in Table 3. The degree score indicates the number of edges that connect to a node, which represents the importance of the target in the network. The betweenness score reflects the extent to which a target serves as a bridge in the network. The closeness score measures the distance between a target and all other targets in the network. The eigenvector score reflects the connectivity of a target with other important targets in the network. The LAC score measures the extent to which a target is clustered in the network. The network score reflects the overall importance of a target in the network. We used the median value of each score to filter the targets twice, resulting in six core targets: AKT1, MMP9, PPARG , MMP2, KDR, and ALB ( Fig. 5A-D). To further investigate their potential as therapeutic targets, we analyzed the expression levels of these targets in breast cancer and normal tissues. Our analysis showed that only AKT1 and MMP9 were significantly upregulated in breast cancer tissues compared to normal tissues, suggesting that they may be the most promising targets for follow-up research (Fig. 5E).  7A-C). According to the widely accepted criterion, small molecule ligands and proteins have good binding when the free energy of binding is less than − 7 kcal/mol. Our results showed that all three proton pump inhibitors had good binding affinities with both AKT1 and MMP9, with binding free energies much lower than − 7 kcal/mol. These results suggested that AKT1 and MMP9 could be potential therapeutic targets for proton pump inhibitors in the treatment of diseases, especially for omeprazole and pantoprazole. Further studies are needed to explore the underlying mechanisms and clinical implications of these findings.

Discussion
Breast cancer and diabetes are two diseases that affect millions of people worldwide. Recent studies have shown a correlation between them, with diabetes being a high-risk factor for breast cancer 30 . In cancers with diabetes, high levels of insulin increase glucose uptake by cancer cells, which further promotes aerobic glycolysis for energy 9,31 . Therefore, drugs that target insulin signaling pathways or glucose metabolism may have potential  www.nature.com/scientificreports/ in treating both diabetes and breast cancer. Additionality, chemotherapy drugs used to treat breast cancer can have an impact on a patient's blood sugar levels, especially for those who have pre-existing diabetes or impaired glucose metabolism. The drugs can interfere with the body's metabolic processes, causing fluctuations in blood sugar levels and potentially worsening diabetes symptoms 32,33 . Chemotherapy drugs can also cause a decrease in insulin production or insulin resistance, which can lead to higher blood sugar levels 34 . This can be particularly problematic for patients with diabetes who are already at risk of developing complications such as nerve damage, cardiovascular disease, and kidney damage. Hence, there is a need for effective treatment options that can address both diseases. Nevertheless, due to the complexity of their underlying mechanisms, there is no single drug that can effectively treat both conditions. Fortunately, proton pump inhibitors (PPIs) have shown potential in treating cancer and diabetes due to their ability to inhibit vacuolar ATPases (V-ATPases) that play a role in lipid metabolism and intracellular pH regulation in cancer cells 35 . Specifically, PPIs can reduce the availability of lipids to induce cancer cell death 36 and inhibit ATPase activity in the pancreas to regulate insulin production and increase insulin sensitivity 37,38 . However, the exact mechanism of PPIs in breast cancer with diabetes is still unclear, and further research is needed to fully understand their effects.
To investigate the potential targets and mechanisms of proton pump inhibitors that exhibit dual effects of anticancer and hypoglycemic, we chose three types of proton pump inhibitors including omeprazole, lansoprazole, and pantoprazole as our research subjects and utilized network pharmacology research methods. We obtained a total of 1838 targets for breast cancer and 417 targets for diabetes from the GeneCards database using a relevance score of ≥ 10. The predicted targets of the PPIs were intersected with the disease targets, resulting in the identification of 33 common targets of PPIs and the two diseases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological processes and pathways involved in the identified genes. We found that the therapeutic targets were mainly enriched in fatty acid transport, cell proliferation, regulation of apoptotic signaling, metabolic processes of small molecules, responses to reactive oxygen species, and the activity of nuclear receptors, ligand − activated transcription, and nuclear hormone receptors. The KEGG pathway analysis revealed several significantly enriched pathways, including prostate cancer, diabetic cardiomyopathy, lipid and atherosclerosis, and insulin resistance. From the enrichment www.nature.com/scientificreports/ results, we know that intersection genes are closely related to the fatty acid transport. As the first link of fatty acid metabolism, fatty acid transport plays a key role in subsequent physiological processes. Yang et al. 39 showed that blocking fatty acid intake could significantly inhibit the occurrence and development of breast cancer. As an important pathway of oxidative energy supply in breast cancer cells, fatty acid metabolism may become a potential therapeutic target for cancer therapy. In addition, we found an interesting phenomenon in which insulin resistance was also a major component of functional enrichment. Fatty acids induce insulin resistance, which is a hallmark of diabetes 40 . The Lipid and atherosclerosis, diabetic cardiomyopathy and other signaling pathways related to diabetes were also significantly enriched in our results. These results suggest that fatty acids may be a potential target for the treatment of breast cancer complicated with diabetes. To further identify the targets of proton pump inhibitors (PPIs), a protein-protein interaction network had constructed using the 33 intersection genes mentioned above. After constructing the regulatory network in Cytoscape software, we used the CytoNCA tool to calculate various network centrality measures for each node, resulting in six core targets: AKT1, MMP9, PPARG, MMP2, KDR, and ALB. We analyzed the expression levels of these targets in breast cancer and normal tissues and found that only AKT1 and MMP9 were significantly upregulated in breast cancer tissues compared to normal tissues, suggesting that they may be the most promising targets for proton pump inhibitors. Finally, we performed molecular docking to investigate the binding affinity of AKT1 and MMP9 with PPIs. We found that all three PPIs had good binding affinities with both AKT1 and MMP9, with binding free energies much lower than − 7 kcal/mol. These results suggest that AKT1 and MMP9 could be potential therapeutic targets for PPIs in the treatment of diseases, especially for omeprazole and pantoprazole. However, further studies are needed to explore the underlying mechanisms and clinical implications of these findings. The article has some limitations that need to be addressed. Firstly, the study only investigated three types of proton pump inhibitors, which may not be representative of all PPIs available in the market. Secondly, the study used a network pharmacology approach, which relies on the accuracy and completeness of the databases used to obtain the target genes. There is a possibility of missing some important genes that may be involved in the www.nature.com/scientificreports/ dual effects of PPIs. Thirdly, the study only focused on breast cancer and diabetes, and the results may not be applicable to other types of cancer or metabolic disorders. Finally, although the study identified six core targets of PPIs that may be potential therapeutic targets, further experimental validation is required to confirm their role in the dual effects of PPIs. Despite these limitations, the study provides valuable insights into the potential targets and mechanisms of PPIs that exhibit dual effects of anti-cancer and hypoglycemic and highlights the importance of fatty acid transport and insulin resistance in these effects. In summary, this study sheds light on the potential therapeutic applications of proton pump inhibitors (PPIs) for breast cancer and diabetes. The research suggests that PPIs may have promising effects on AKT1 and MMP9, and that omeprazole and pantoprazole may be effective in treating these diseases. The study also identified several biological processes and pathways that may be involved in the action of PPIs, as well as the regulatory network and molecular docking analyses that support the clinical utility of AKT1 and MMP9 as therapeutic targets. However, further research is required to confirm these findings and explore the underlying mechanisms. Overall, these results have important implications for the development of novel therapeutic strategies for breast cancer and diabetes.